Back-Propagation Image Visual Saliency Detection Method Based On Depth Image Mining

ABSTRACT

A back-propagation significance detection method based on depth map mining, comprising: for an input image Io, at a preprocessing phase, obtaining a depth image Id and an image Cb with four background corners removed of the image Io; at a first processing phase, carrying out positioning detection on a significant region of the image by means of the obtained image Cb with four background corners removed and the obtained depth image Id to obtain the preliminary detection result S1 of a significant object in the image; then carrying out depth mining on a plurality of processing phases of the depth image Id to obtain corresponding significance detection results; and then optimizing the significance detection result mined in each processing phase by means of a back-propagation mechanism to obtain a final significance detection result map. The method can improve the detection accuracy of the significance object.

TECHNICAL FIELD

The present invention relates to the technical field of imageprocessing, in particular to a multi-phase back-propagation image visualsaliency detection algorithm for deep mining of a depth image.

BACKGROUND ART

In a complex scene, human would be quickly attracted to a few of salientvisual objects and process these objects preferentially with eyes. Thisprocess is called visual saliency. The saliency detection is just tosimulate human eyes to properly process images with a mathematicalcalculation method by using a visually biological mechanism of the humaneyes, so as to obtain a salient object in one picture. Since we canpreferentially distribute computing resources required by image analysisand synthesis through salient regions, it is significant to detect thesalient regions of the images by calculation. The extracted salientimages can be widely used in many computer vision fields, includingimage segmentation of target objects of interest, detection andrecognition of the target objects, image compression and encoding, imageretrieval, content-aware image editing and the like.

Generally, an existing saliency detection framework is mainly dividedinto: a from-bottom-to-top saliency detection method and afrom-top-to-bottom saliency detection method. The data driving-basedfrom-bottom-to-top saliency detection method is adopted mostly at thepresent and independent from specific tasks. The from-top-to-bottomsaliency detection method is subjected to consciousness and associatedwith specific tasks.

In the existing methods, most of the from-bottom-to-top saliencydetection methods use low-level characteristic information, such ascolor characteristics, distance characteristics and some heuristicsaliency characteristics. Although these methods have their ownadvantages, they are not accurate and robust enough on challenging datasets in specific scenes. In order to solve this problem, with the adventof a 3D (3-dimensional) image acquisition technology, there are methodsadopting depth information to enhance the accuracy of salient objectdetection at the present. The depth information can increase theaccuracy of the salient object detection, but when one salient objecthas a low contrast with its background, the accuracy of the saliencydetection will still be affected. On the whole, the existing imagesalient object detection methods have low accuracy during detection ofthe salient objects, are not robust enough and may easily cause errordetection, missing detection and the like, and it is very hard to obtainan accurate image saliency detection result, resulting in falsedetection of a salient object body and also causing a certain error toan application using a saliency detection result.

SUMMARY OF THE INVENTION

The present invention is directed to provide a back-propagation saliencydetection algorithm for deep mining of a depth image for theabovementioned shortcomings in the prior art, so as to solve theproblems that existing saliency detection is not accurate and robustenough, allow a salient region in an image to be displayed moreaccurately and provide accurate and useful information for laterapplications such as target recognition and classification.

A technical solution provided by the present invention is:

A back-propagation saliency detection method based on depth imagemining, including: at a preprocessing phase, obtaining a depth image ofan image and an image with four background corners removed; at a firstprocessing phase, carrying out positioning detection on a salient regionof the image by means of color, depth and distance information to obtaina preliminary detection result of a salient object in the image; thencarrying out deep mining on the depth image from a plurality of layers(processing phases) to obtain corresponding saliency detection results;and then optimizing the saliency detection result mined in each layer bymeans of a back-propagation mechanism to obtain a final saliencydetection result image. The implementation of the method includes thefollowing steps:

1) A preprocessing phase: for an input image I_(o), firstly obtaining adepth image, defined as I_(d), by means of Kinect equipment; andsecondly, removing four background edges of the image by means of a BSCAalgorithm, and defining the obtained image with the four backgroundcorners removed as C_(b), wherein the BSCA algorithm is recorded in thedocument (Qin Y, Lu H, Xu Y, et al. Saliency detection via CelluarAutomata [C]//IEEE Conference on Computer Vision and PatternRecognition. IEEE, 2015:110-119, and is to obtain a background saliencymap based on background seed information according to color and distanceinformation comparison.

2) A first processing phase: carrying out preliminary saliency detectionon the input image I_(o) by means of the obtained image C_(b) with thefour background corners removed and the depth image I_(d) to obtain apreliminary saliency monitoring result defined as: S₁;

specifically including steps 11 to 15:

Step 11, dividing the image into K regions by means of a K-meansalgorithm, and calculating a color saliency value S_(c)(r_(k)) of eachsubregion through the formula (1):

S _(c)(r _(k))=Σ_(i=1,i≠k) ^(K) P _(i) W _(s)(r _(k))D _(c)(r _(k) ,r_(i))  (1)

wherein r_(k) and r_(i) respectively represent regions k and i,D_(c)(r_(k), r_(i)) represents a Euclidean distance of the region k andthe region i in an L*a*b color space, P_(i) represents a proportion ofthe region i to an image region; W_(s)(r_(k)) is defined as follows:

$\begin{matrix}{{W_{s}\left( r_{k} \right)} = e^{- \frac{D_{o}{({r_{k},r_{i}})}}{\sigma^{2}}}} & (2)\end{matrix}$

wherein D_(o)(r_(k), r_(i)) represents a coordinate position distance ofthe region k and the region i, and σ is a parameter controlling therange of the W_(s)(r_(k)).

Step 12, by means of the color saliency value calculating mode,calculating a depth saliency value S_(d)(r_(k)) of the depth imagethrough the formula (3):

$\begin{matrix}{{S_{d}\left( r_{k} \right)} = {\sum\limits_{{i = 1},{i \neq k}}^{K}{P_{i}{W_{s}\left( r_{k} \right)}{D_{d}\left( {r_{k},r_{i}} \right)}}}} & (3)\end{matrix}$

wherein D_(d)(r_(k), r_(i)) represents a Euclidean distance of theregion k and the region i in a depth space. Step 13, calculating acenter and a depth weight S_(s)(r_(k)) of the region k through theformula (4), wherein generally, a salient object is located in thecenter:

$\begin{matrix}{{S_{s}\left( r_{k} \right)} = {\frac{G\left( {{P_{k} - P_{o}}} \right)}{N_{k}}{W_{d}\left( d_{k} \right)}}} & (4)\end{matrix}$

wherein G(⋅) represents Gaussian normalization, ∥⋅∥ represents Euclideandistance operation, P_(k) is a position coordinate of the region k,P_(o) is a coordinate center of the image, and N_(k) is the number ofpixels of the region k. W_(d)(d_(k)) is a depth weight, defined asfollows:

W _(d)(d _(k))=(max{d}−d _(k))^(μ)  (5)

wherein max{d} represents a maximum depth of the depth image, d_(k)represents a depth value of the region k, and u is a parameter relatedto the calculated depth image, defined as follows:

$\begin{matrix}{\mu = \frac{1}{{\max\left\{ d \right\}} - {\min\left\{ d \right\}}}} & (6)\end{matrix}$

wherein min{d} represents a minimum depth of the depth image.

Step 14, obtaining a coarse saliency detection result S_(fc)(r_(k)) bymeans of the formula (7), which is a non-optimized preliminary saliencydetection result at the first processing phase:

S _(fc)(r _(k))=G(S _(c)(r _(k))S _(s)(r _(k))+S _(d)(r _(k))S _(s)(r_(k)))  (7)

Step 15, in order to optimize the preliminary saliency detection result,enhancing the result of the formula (7) by means of the depth imageI_(d)(d_(k)) and the image C_(b) with the four background cornersremoved at the preprocessing phase, as shown in the formula 8:

S ₁(r _(k))=s _(fc)(r _(k))×¬I _(d)(d _(k))×c _(b)  (8)

S₁(r_(k)) represents an optimized result of the S_(fc)(r_(k)) of theformula 7, namely an optimized detection result at the first processingphase;

3) A second processing phase: converting the depth image into a colorimage, and obtaining a medium saliency detection result, defined as S₂,by means of the calculating process of the first processing phase andoptimization of the back-propagation mechanism.

3) A third processing phase: carrying out background filtering on thedepth image, converting the filtered depth image into a color image, andobtaining a final saliency detection result S by means of thecalculating process of the second processing phase and optimization ofthe back-propagation mechanism.

Compared with the prior art, the present invention has the beneficialeffects that:

the present invention provides the multi-layer back-propagation saliencydetection algorithm based on depth image mining, including: firstly, ata preprocessing phase, obtaining the depth image of the image and theimage with four background corners removed; secondly, calculating thepreliminary saliency detection result based on information such as thecolor, the space and the depth of the image by means of the saliencydetection algorism of the first layer; then carrying out deep mining onthe depth image from the second layer and the third layer, and carryingout saliency detection by means of the calculating mode of the firstlayer; and finally, optimizing the saliency detection results of thesecond and third layers by means of the back-propagation mechanism toobtain the secondary saliency detection result image and the finalsaliency detection result image.

The present invention can detect the salient object more accurately androbustly. Compared with the prior art, the present invention has thefollowing technical advantages:

(I) by multi-layer mining of the depth image, the present invention canimprove the accuracy of salient object detection; and

(II) the present invention provides the back-propagation mechanism tooptimize the saliency detection result of each layer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flowchart provided by the present invention.

FIG. 2 shows a comparison diagram of detection result images, obtainedby detecting an input image by respectively adopting an existing methodand the method of the present invention, and images expected to beobtained via artificial calibration according to an embodiment of thepresent invention,

wherein the first column displays the input images; the second columndisplays the images expected to be obtained via the artificialcalibration; the third column displays the detection result images ofthe present invention; and the columns from four to ten are detectionresult images obtained by means of other existing methods.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is further described below through embodiments incombination with drawings, but the scope of the present invention willnot be limited in any mode.

The present invention provides a multi-layer back-propagation saliencydetection algorithm based on depth image mining, which can detect asalient object more accurately and robustly. In the present invention,firstly, at a preprocessing layer/phase, a depth image of an image andan image with four background corners removed are obtained; secondly, ata first layer, second layer and third layer, the depth image is minedrespectively to obtain corresponding saliency detection results; andfinally, a back-propagation mechanism is used to optimize the saliencydetection results of the various layers/processing phases to obtain afinal saliency detection result image. FIG. 1 is a flowchart of asalient object detection method provided by the present invention,including the following steps that:

Step I, an image I_(o) to be detected is input, and an image C_(b) withfour background corners removed and a depth image I_(d) of the image areobtained;

wherein the image C_(b) with four background corners removed is obtainedby means of a BSCA algorithm recorded in the document (Qin Y, Lu H, XuY, et al. Saliency detection via Celluar Automata [C]//IEEE Conferenceon Computer Vision and Pattern Recognition. IEEE, 2015:110-119, and thedepth image I_(d) of the image is obtained by means of Kinect equipment;first layer of operation of the algorithm: (Steps II to VI)

Step II, the image is divided into K regions by means of a K-meansalgorithm, and a color saliency value S_(c)(r_(k)) of each subregion iscalculated through the formula (1):

S _(c)(r _(k))=Σ_(i=1,i≠k) ^(K) P _(i) W _(s)(r _(k))D _(c)(r _(k) ,r_(i))  (1)

wherein r_(k) and r_(i) respectively represent regions k and i,D_(c)(r_(k), r_(i)) represents a Euclidean distance of the region k andthe region i in an L*a*b color space, P_(i) represents a proportion ofthe region i to an image region; W_(s)(r_(k)) is defined as follows:

$\begin{matrix}{{W_{s}\left( r_{k} \right)} = e^{- \frac{D_{o}{({r_{k},r_{i}})}}{\sigma^{2}}}} & (2)\end{matrix}$

wherein D_(o)(r_(k), r_(i)) represents a coordinate position distance ofthe region k and the region i, and σ is a parameter controlling therange of the W_(s)(r_(k));

Step III, by means of the color saliency value calculating mode, a depthsaliency value S_(d)(r_(k)) of the depth image is calculated through theformula (3):

$\begin{matrix}{{S_{d}\left( r_{k} \right)} = {\sum\limits_{{i = 1},{i \neq k}}^{K}{P_{i}{W_{s}\left( r_{k} \right)}{D_{d}\left( {r_{k},r_{i}} \right)}}}} & (3)\end{matrix}$

wherein D_(d)(r_(k), r_(i)) represents a Euclidean distance of theregion k and the region i in a depth space. Step IV, calculating acenter and a depth weight S_(s)(r_(k)) through the formula (4), whereingenerally, a salient object is located in the center:

$\begin{matrix}{{S_{s}\left( r_{k} \right)} = {\frac{G\left( {{P_{k} - P_{o}}} \right)}{N_{k}}{W_{d}\left( d_{k} \right)}}} & (4)\end{matrix}$

wherein G(⋅) represents Gaussian normalization, ∥⋅∥ represents Euclideandistance operation, P_(k) is a position coordinate of the region k,P_(o) is a coordinate center of the image, and N_(k) is the number ofpixels of the region k. W_(d)(d_(k)) is a depth weight, defined asfollows:

W _(d)(d _(k))=(max{d}−d _(k))^(μ)  (5)

wherein max{d} represents a maximum depth of the depth image, d_(k)represents a depth value of the region k, and u is a parameter relatedto the calculated depth image, defined as follows:

$\begin{matrix}{\mu = \frac{1}{{\max\left\{ d \right\}} - {\min\left\{ d \right\}}}} & (6)\end{matrix}$

wherein min{d} represents a minimum depth of the depth image;

Step V, a coarse saliency detection result S_(fc)(r_(k)) is obtained bymeans of the formula (7), which is a non-optimized preliminary saliencydetection result at the first processing phase:

S _(fc)(r _(k))=G(S _(c)(r _(k))S _(s)(r _(k))+S _(d)(r _(k))S _(s)(r_(k)))  (7);

Step VI, in order to optimize the preliminary saliency detection result,the result of the formula (7) is enhanced by means of the depth imageI_(d)(d_(k)) and the image C_(b) with the four background cornersremoved at the preprocessing phase, as shown in the formula 8:

S ₁(r _(k))=s _(fc)(r _(k))×¬I _(d)(d _(k))×c _(b)  (8)

S₁(r_(k)) represents an optimized result of the S_(fc)(r_(k)) of theformula 7, namely an optimized detection result at the first processingphase;

second layer of operation of the algorithm: (Steps VII to IX)

Step VII, the depth image is further mined: firstly, the depth image isextended into a depth-based color image through the formula (9):

I _(e)

R|G|B

=I _(o)

R|G|B

×I _(d)  (9)

wherein I_(e) is the extended depth-based color image;

Step VIII, the operations in the steps II to V of the first layer arecarried out on the extended depth-based color image to obtain a coarsesaliency detection result S_(sc)(r_(k)):

S _(sc)(r _(k))=G(S _(c)(r _(k))S _(s)(r _(k))+S _(d)(r _(k))S _(s)(r_(k))),  (10);

Step IX, in order to optimize the coarse saliency detection result, aback-propagation mechanism is used to optimize the coarse saliencydetection result by means of the preliminary detection result (theresult calculated through the formula (7)) of the first layer throughthe formula (11), so as to obtain a medium saliency detection resultS₂(r_(k)):

$\begin{matrix}{\mspace{79mu}{{{{S_{2}\left( r_{k} \right)} = {{S_{1}^{2}\left( r_{k} \right)} + {{S_{1}\left( r_{k} \right)}\left( {1 - \text{?}} \right)}}};}{\text{?}\text{indicates text missing or illegible when filed}}}} & (11)\end{matrix}$

third layer of operation of the algorithm: (Steps X to XIII)

Step X, the depth image is further mined: firstly, background filteringprocessing is carried out on the depth image by means of the formula(12) to obtain a filtered depth image I_(df):

$\begin{matrix}{I_{df} = \left\{ \begin{matrix}{I_{d},} & {d \leq {\beta \times \max\left\{ d \right\}}} \\{0,} & {d > {\beta \times \max\left\{ d \right\}}}\end{matrix} \right.} & (12)\end{matrix}$

wherein I_(d) is the depth image with the background filtered;

Step XI, the filtered depth image is extended into a color image,defined as I_(e)f, through the operation of the formula (9) of the stepVII of the second layer;

Step XII, the color image I_(ef) of the filtered depth image is operatedthrough the steps II to V of the first layer to obtain a coarse saliencydetection result S_(tc)(r_(k)) of the third layer:

S _(tc)(r _(k))=G(S _(c)(r _(k))S _(s)(r _(k))+S _(d)(r _(k))S _(s)(r_(k)))  (13);

Step VIII, in order to optimize the coarse saliency detection result,the back-propagation mechanism is used to optimize the coarse detectionresult of the third layer by means of the preliminary detection resultsof the first layer and the second layer through the formula (14) toobtain a final saliency detection result S(r_(k)):

$\begin{matrix}{\mspace{79mu}{{{S\left( r_{k} \right)} = {{S_{2}\left( r_{k} \right)}\left( {{S_{2}\left( r_{k} \right)} + {S_{tc}\left( r_{k} \right)}} \right)\left( {{S_{tc}\left( r_{k} \right)} + 1 - \text{?}} \right)}}{\text{?}\text{indicates text missing or illegible when filed}}}} & (14)\end{matrix}$

FIG. 2 shows detection result images, obtained by detecting an inputimage by respectively adopting an existing method and the method of thepresent invention, and images expected to be obtained via artificialcalibration, wherein the first column displays the input images; thesecond column displays the images expected to be obtained via theartificial calibration; the third column displays the detection resultimages of the present invention; and the columns from four to ten aredetection result images obtained by means of other existing methods.Through comparison with the images of FIG. 2, it can be seen thatcompared with other methods, the method of the present invention candetect the salient object, is lowest in error rate and highest inaccuracy and has extremely good robustness.

It should be noted that the embodiments are disclosed to help to furtherunderstand the present invention, but those skilled in the art canunderstand that various replacements and modifications are possiblewithout departing from the spirit and scope of the present invention andattached clamps. Therefore, the present invention should not be limitedto the contents disclosed by the embodiments. The protection scope ofthe present invention is based on the scope defined by claims.

1. A back-propagation saliency detection method based on depth imagemining, comprising, for an input image I_(o): at a preprocessing phase,obtaining a depth image I_(d) of an image I_(o) and an image C_(b) withfour background corners removed; at a first processing phase, carryingout positioning detection on a salient region of the image by means ofthe obtained image C_(b) with four background corners removed and thedepth image I_(d) to obtain a preliminary detection result S₁ of asalient object in the image; then carrying out deep mining on the depthimage I_(d) from a plurality of processing phases to obtaincorresponding saliency detection results; and then optimizing thesaliency detection result mined in each processing phase by means of aback-propagation mechanism to obtain a final saliency detection resultimage.
 2. The back-propagation saliency detection method based on depthimage mining of claim 1, wherein the depth image I_(d) is specificallyobtained by means of Kinect equipment; and four background edges of theimage are removed by means of a BSCA algorithm to obtain the image C_(b)with four background corners removed.
 3. The back-propagation saliencydetection method based on depth image mining of claim 1, whereinprocessing at the first processing phase specifically comprises steps 11to 15: Step 11, dividing the image into K regions by means of a K-meansalgorithm, and calculating a color saliency value S_(c)(r_(k)) of eachsubregion through the formula (1):S _(c)(r _(k))=Σ_(i=1,i≠k) ^(K) P _(i) W _(s)(r _(k))D _(c)(r _(k) ,r_(i))  (1) wherein r_(k) and r_(i) respectively represent regions k andi, D_(c)(r_(k), r_(i)) represents a Euclidean distance of the region kand the region i in an L*a*b color space, P_(i) represents a proportionof the region i to an image region; W_(s)(r_(k)) is obtained through theformula (2): $\begin{matrix}{{W_{s}\left( r_{k} \right)} = e^{- \frac{D_{o}{({r_{k},r_{i}})}}{\sigma^{2}}}} & (2)\end{matrix}$ wherein D_(o)(r_(k), r_(i)) represents a coordinateposition distance of the region k and the region i, and σ is a parametercontrolling the range of the W_(s)(r_(k)); Step 12, calculating a depthsaliency value S_(d)(r_(k)) of the depth image through the formula (3):$\begin{matrix}{{S_{d}\left( r_{k} \right)} = {\sum\limits_{{i = 1},{i \neq k}}^{K}{P_{i}{W_{s}\left( r_{k} \right)}{D_{d}\left( {r_{k},r_{i}} \right)}}}} & (3)\end{matrix}$ wherein D_(d)(r_(k), r_(i)) represents a Euclideandistance of the region k and the region i in a depth space; Step 13,calculating a center and a depth weight S_(s)(r_(k)) of the region kthrough the formula (4): $\begin{matrix}{{S_{s}\left( r_{k} \right)} = {\frac{G\left( {{P_{k} - P_{o}}} \right)}{N_{k}}{W_{d}\left( d_{k} \right)}}} & (4)\end{matrix}$ wherein G(⋅) represents Gaussian normalization, ∥⋅∥represents Euclidean distance operation, P_(k) is a position coordinateof the region k, P_(o) is a coordinate center of the image, and N_(k) isthe number of pixels of the region k; W_(d)(d_(k)) is a depth weight,calculated through the formula (5):W _(d)(d _(k))=(max{d}−d _(k))^(μ)  (5) wherein max{d} represents amaximum depth of the depth image, d_(k) represents a depth value of theregion k, and u is a parameter related to the calculated depth image,calculated through the formula (6): $\begin{matrix}{\mu = \frac{1}{{\max\left\{ d \right\}} - {\min\left\{ d \right\}}}} & (6)\end{matrix}$ wherein min{d} represents a minimum depth of the depthimage; Step 14, obtaining a coarse saliency detection resultS_(fc)(r_(k)) by means of the formula (7):S _(fc)(r _(k))=G(S _(c)(r _(k))S _(s)(r _(k))+S _(d)(r _(k))S _(s)(r_(k)))  (7) wherein the saliency detection result S_(fc)(r_(k)) is apreliminary saliency detection result obtained at the first processingphase; Step 15, in order to optimize the preliminary saliency detectionresult, enhancing the result of the formula (7) by means of the depthimage I_(d)(d_(k)) and the image C_(b) with the four background cornersremoved at the preprocessing phase, as shown in the formula 8:S ₁(r _(k))=s _(fc)(r _(k))×¬I _(d)(d _(k))×c _(b)  (8) namely, anoptimized detection result S₁(r_(k)) at the first processing phase isobtained.
 4. The back-propagation saliency detection method based ondepth image mining of claim 3, wherein a step of carrying out deepmining on the depth image I_(d) from a plurality of processing phasescomprises a second processing phase and a third processing phase;processing at the second processing phase specifically comprises steps21 to 23: Step 21, extending the depth image I_(d) into a depth-basedcolor image I_(e) through the formula (9):I _(e)

R|G|B

=I _(o)

R|G|B

×I _(d)  (9) wherein I_(e) is the extended depth-based color image; Step22, using the method of carrying out positioning detection on a salientregion of the image at the first processing phase for the extendeddepth-based color image I_(e) to obtain a coarse saliency detectionresult S_(sc)(r_(k)) at the second processing phase, expressed as theformula (10):S _(sc)(r _(k))=G(S _(c)(r _(k))S _(s)(r _(k))+S _(d)(r _(k))S _(s)(r_(k))),  (10); Step 23, using a back-propagation mechanism to optimizethe coarse saliency detection result of the second processing phase instep 22 by means of the preliminary detection result of the firstprocessing phase, so as to obtain a medium saliency detection resultS₂(r_(k)) through the formula (11): $\begin{matrix}{\mspace{79mu}{{{{S_{2}\left( r_{k} \right)} = {{S_{1}^{2}\left( r_{k} \right)} + {{S_{1}\left( r_{k} \right)}\left( {1 - \text{?}} \right)}}};}{\text{?}\text{indicates text missing or illegible when filed}}}} & (11)\end{matrix}$ processing at the third processing phase specificallycomprises steps 31 to 34: Step 31, further mining the depth image:firstly, carrying out background filtering processing on the depth imageby means of the formula (12) to obtain a filtered depth image I_(df):$\begin{matrix}{I_{df} = \left\{ \begin{matrix}{I_{d},} & {d \leq {\beta \times \max\left\{ d \right\}}} \\{0,} & {d > {\beta \times \max\left\{ d \right\}}}\end{matrix} \right.} & (12)\end{matrix}$ wherein I_(df) is the depth image with the backgroundfiltered; Step 32, extending the filtered depth image into a color imagethrough the formula (9), defined as I_(ef); Step 33, using the method ofcarrying out positioning detection on a salient region of the image atthe first processing phase for the color image I_(ef) of the filtereddepth image to obtain a coarse saliency detection result S_(tc)(r_(k))of the third processing phase, expressed as the formula (13):S _(tc)(r _(k))=G(S _(c)(r _(k))S _(s)(r _(k))+S _(d)(r _(k))S _(s)(r_(k)))  (13); Step 34, using the back-propagation mechanism to optimizethe coarse detection result of the third processing phase by means ofthe preliminary detection results of the first processing phase and thesecond processing phase to obtain a saliency detection result S(r_(k))through the formula (14): $\begin{matrix}{\mspace{79mu}{{{S\left( r_{k} \right)} = {{S_{2}\left( r_{k} \right)}\left( {{S_{2}\left( r_{k} \right)} + {S_{tc}\left( r_{k} \right)}} \right)\left( {{S_{tc}\left( r_{k} \right)} + 1 - \text{?}} \right)}}{\text{?}\text{indicates text missing or illegible when filed}}}} & (14)\end{matrix}$ thus, a final saliency detection result image is obtained.